Abstract
Grouping problems arise in many applications, and the aim is to partition a set U of items, into a collection of mutually disjoint subsets or groups. The objective of grouping is to optimize a cost function such as to minimize the number of groups. Problems in this category may come from many different domains such as graph coloring, bin packing, cutting stock, and scheduling. This investigation is related in particular to scheduling transportation events, modeled as a grouping problem, and with the objective to minimize the number of vehicles used and satisfying the customer demand. There is a set of events to be scheduled (items) into a set of vehicles (groups). Of course, there are constraints that forbid assigning all events to a single vehicle. Two different techniques are used in this work to tackle the problem: Grouping Genetic Algorithms and an algorithm based on the heuristic DJD widely used for solving bin packing problems. Both methods were adapted to the problem and compared to each other using a set of randomly generated problem instances designed to comply with real situations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Falkenauer, E.: Genetic Algorithms and Grouping Problems. Wiley, Chichester (1998)
Garey, M.R., Johnson, D.S.: Computers and Intractability. W.H. Freeman and Company, New York (1979)
Hang, X., Zhi-Long, C., Srinivas, R., Sundar, A.: Solving a practical pickup and delivery problem. Transportation Science 37(3) (2003)
Savelsbergh, M.W.P., Sol, M.: The general pickup and delivery problem. Transportation Science 29(1) (1995)
Sakawa, M., Nishizaki, I., Uemura, Y.: Interactive fuzzy programming for a decentralized two-level transportation planning and work force assignment problem. IEEE, Los Alamitos (2001)
Rich, J.L.: A Computational Study of Vehicle Routing Applications. PhD thesis, Rice University (1999)
Ross, P., Schulenberg, S., Marín-Blázquez, J.G., Hart, E.: Hyper-heuristics: learning to combine simple heuristics in bin-packing problem. In: Genetic and Evolutionary Computation Conference (2002)
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Adison Wesley (1989)
Djang, P.A., Finch, P.R.: Solving one dimension bin-packing problems. Journal of Heuristics (1998)
Beasley, J.E.: Beasley operations research library. Collection of problems for packing and cutting (2003)
Scholl, A., Klein, R.: Bin packing library. Collection of bin packing problems (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Terashima-Marín, H., Tavernier-Deloya, J.M., Valenzuela-Rendón, M. (2005). Scheduling Transportation Events with Grouping Genetic Algorithms and the Heuristic DJD. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_19
Download citation
DOI: https://doi.org/10.1007/11579427_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29896-0
Online ISBN: 978-3-540-31653-4
eBook Packages: Computer ScienceComputer Science (R0)